Triple
T8020627
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pennsylvania state historic sites |
E186730
|
entity |
| Predicate | includesFeatures |
P53912
|
FINISHED |
| Object | historic buildings |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: historic buildings | Statement: [Pennsylvania state historic sites, includesFeatures, historic buildings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesFeatures Context triple: [Pennsylvania state historic sites, includesFeatures, historic buildings]
-
A.
featuresSupporter
Indicates that one entity serves as a supporter, advocate, or promoter of another entity or its cause.
-
B.
supportsFeature
Indicates that one entity provides, enables, or is compatible with a particular feature or capability of another.
-
C.
mayIncludeFeature
chosen
Indicates that one entity is allowed or able to contain, incorporate, or be associated with a particular feature.
-
D.
featuresSuit
Indicates that one entity includes or presents a particular suit (e.g., clothing, armor, or outfit) as a notable component or attribute.
-
E.
featuresCross
Indicates that one feature or element intersects or passes across another in space or structure.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca82ac7fc081909b1398cf025423af |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3e8d90488190b57d1e748e272061 |
completed | March 31, 2026, 3:25 a.m. |
| PD | Predicate disambiguation | batch_69cb049253d08190bafcecfde493ab8b |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:20 p.m.